arXiv subjects

Torbjörn Wigren

Publications and source records attributed to Torbjörn Wigren.

8 recordsLinked to original sources

arXiv2026

Obstacle-Aware Online Receiver Planning in Multistatic Ranging

Multistatic ranging with mobile receivers enables good tracking performance due to the combined adaptive sensor geometry. However, environments that contain signal obstructing obstacles require receiver trajectory planning to maintain line-of-sight (LOS) conditions with transmitters and the target of interest. In this letter, we develop a non-myopic receding-horizon framework for multistatic tracking. It uses convex collision-avoidance constraints and a control objective that focuses on maintaining good LOS signal conditions, taking into account future obstructions. We demonstrate the efficiency and tracking accuracy of the method via a numerical experiment.

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arXiv2026

Detecting Feedback-path Delay Injection Attacks Using Interacting Multiple Model Filtering

Time-delays are known to have a detrimental effect on feedback systems. In the context of networked cyber-physical systems, delays can be injected by malicious adversaries. Detecting them early is an important challenge. This paper proposes a novel variation of Interacting Multiple Model filtering to detect delay injection attacks in feedback control systems, when hidden in an open loop setting. The detection scheme is formalised by treating delay as alternative modes of the system, and theoretical analysis of the stationary distribution informs a reduction to a three parameter model as well as the choices of hyper parameter values. The method is evaluated on a cruise control application, and shows detection within a few seconds and a low false alarm probability.

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arXiv2026

Recursive Experiment Design for Closed-Loop Identification of ARMAX Systems with Output Perturbation Limits

In many applications, system identification experiments must be performed in closed loop to ensure safety or to maintain system operation. In this paper, we consider the recursive design of informative experiments for ARMAX models by adding a bounded probing signal to the input generated by a fixed output feedback controller. The resulting output perturbations should be kept within user-specified limits. We analyze the identifiability and feasibility conditions of this setting and then proceed to derive a probing signal that can be efficiently computed in closed form. We demonstrate the effectiveness and properties of the design in numerical experiments.

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arXiv2025

Adaptive Experiment Design for Nonlinear System Identification with Operational Constraints

We consider the joint problem of online experiment design and parameter estimation for identifying nonlinear system models, while adhering to system constraints. We utilize a receding horizon approach and propose a new adaptive input design criterion, which is tailored to continuously updated parameter estimates, along with a new sequential estimator. We demonstrate the ability of the method to design informative experiments online, while steering the system within operational constraints.

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arXiv2025

Convergence in On-line Learning of Static and Dynamic Systems

The paper derives analytical expressions for the asymptotic average updating direction of the adaptive moment generation (ADAM) algorithm when applied to recursive identification of nonlinear systems. It is proved that the standard hyper-parameter setting results in the same asymptotic average updating direction as a diagonally power normalized stochastic gradient algorithm. With the internal filtering turned off, the asymptotic average updating direction is instead equivalent to that of a sign-sign stochastic gradient algorithm. Global convergence to an invariant set follows, where a subset of parameters contain those that give a correct input-output description of the system. The paper also exploits a nonlinear dynamic model to embed structure in recurrent neural networks. A Monte-Carlo simulation study validates the results.

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arXiv2023

Observer-Feedback-Feedforward Controller Structures in Reinforcement Learning

The paper proposes the use of structured neural networks for reinforcement learning based nonlinear adaptive control. The focus is on partially observable systems, with separate neural networks for the state and feedforward observer and the state feedback and feedforward controller. The observer dynamics are modelled by recurrent neural networks while a standard network is used for the controller. As discussed in the paper, this leads to a separation of the observer dynamics to the recurrent neural network part, and the state feedback to the feedback and feedforward network. The structured approach reduces the computational complexity and gives the reinforcement learning based controller an {\em understandable} structure as compared to when one single neural network is used. As shown by simulation the proposed structure has the additional and main advantage that the training becomes significantly faster. Two ways to include feedforward structure are presented, one related to state feedback control and one related to classical feedforward control. The latter method introduces further structure with a separate recurrent neural network that processes only the measured disturbance. When evaluated with simulation on a nonlinear cascaded double tank process, the method with most structure performs the best, with excellent feedforward disturbance rejection gains.

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arXiv2023

Robust nonlinear set-point control with reinforcement learning

There has recently been an increased interest in reinforcement learning for nonlinear control problems. However standard reinforcement learning algorithms can often struggle even on seemingly simple set-point control problems. This paper argues that three ideas can improve reinforcement learning methods even for highly nonlinear set-point control problems: 1) Make use of a prior feedback controller to aid amplitude exploration. 2) Use integrated errors. 3) Train on model ensembles. Together these ideas lead to more efficient training, and a trained set-point controller that is more robust to modelling errors and thus can be directly deployed to real-world nonlinear systems. The claim is supported by experiments with a real-world nonlinear cascaded tank process and a simulated strongly nonlinear pH-control system.

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arXiv2023

Aiding reinforcement learning for set point control

While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems. One reason for this is that the learned controller may not be able to excite the system dynamics well enough initially, and therefore it can take a long time to get data that is informative enough to learn for good control. The paper contributes by augmentation of reinforcement learning with a simple guiding feedback controller, for example, a proportional controller. The key advantage in set point control is a much improved excitation that improves the convergence properties of the reinforcement learning controller significantly. This can be very important in real-world control where quick and accurate convergence is needed. The proposed method is evaluated with simulation and on a real-world double tank process with promising results.

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